As businesses accelerate the implementation of AI, the importance of data accountability cannot be overstated. Big tech companies, in particular, are facing high demand for data in order to develop new AI capabilities. To meet these increasing business needs, it is crucial to optimize data processes and ensure data cleanliness without compromising on standards.
IBM, a leading tech company experiencing a surge in demand for its services, is taking steps to enhance its approach to data governance. The company, known for building AI systems for various applications, relies on substantial amounts of data to train and test its models. In an effort to bolster trust in its data practices, IBM is working on improving its data governance processes.
One key aspect of this improvement is the co-creation of the Data Provenance Standards, a set of cross-industry standards for metadata. Developed in collaboration with 19 other companies, including the Data & Trust Alliance, these standards aim to provide information about the origin, lineage, and suitability of data for specific purposes. By establishing universal data transparency standards, the initiative seeks to promote a culture of trust in enterprise AI.
By aligning data standards, businesses can better access a diverse array of high-quality data efficiently. This is crucial for developing trustworthy AI that can deliver measurable results for organizations. A recent report from Cloudera underscores the importance of unifying data lifecycles for AI and analytics development, with 90% of IT leaders recognizing its critical role.
Christina Montgomery, IBM’s chief privacy and trust officer, emphasizes the potential of AI to drive productivity and societal benefits. However, she also warns about the risks associated with irresponsible AI design and deployment. IBM’s data governance program is centered on a meticulous data authorization process, reflecting the company’s commitment to managing, understanding, and safeguarding data used in AI models.
In conclusion, as businesses increasingly adopt AI technologies, maintaining robust data governance practices is vital. Through initiatives like the Data Provenance Standards, companies can foster a culture of trust and accountability in the development and deployment of AI. By leveraging clean and standardized data, organizations can pave the way for a positive future where AI benefits both individuals and society at large.
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https://technologymagazine.com/data-and-data-analytics/ibms-commitment-to-data-governance-in-line-with-ai-use